Diagnosing malaria from some symptoms: a machine learning approach and public health implications

Malaria is a leading cause of death in Nigeria and remains a public health concern because of the increasing resistance of the disease to antimalarial drugs. Pregnant women and children under five years of age are the most vulnerable. Efforts to eradicate malaria is often frustrated due to some various sociodemographic factors and medical factors. One of the vital therapeutic factors is misdiagnosis. Hence, the paper applied different data mining models to diagnose malaria using fifteen symptoms of patients that attended a hospital in Nigeria. The data were obtained from a peer reviewed data article that comprises 337 subjects at Federal Polytechnic Ilaro Medical Centre, Ogun State. The independent variables are 15 symptoms, age, and sex, while the target or dependent is the outcome. The outcome is the result of the diagnosis, which is positive for negative for malaria. Eight machine learning tools were applied to the data on the Orange Software platform. Weak non-significant correlations were obtained between the 15 symptoms and the outcome, and hence no pattern was observed. However, the application of data mining tools revealed a hidden pattern that correctly predicted the outcome using the subjects' symptoms, age, and sex. 6 out of the 8 machine learning models were adjudged to perform well using different performance metrics. The Adaptive boosting model gave a percent 100% precision in the classification, and logistic regression was the least. Furthermore, a percent performance of Adaboost implies that the model correctly predicted all the 221 true negatives and 116 true positives with a misclassification (misdiagnosis) of zero. Classify using only the 15 symptoms reduced the predictive accuracy of the 6 models. Nevertheless, Adaboost performance was the best with a classification accuracy of 98.2%, precision of 96.6%, and an error rate of just 1.8%. Again, logistic regression performance was the least. The present work has presented a strong relationship between age and sex and the outcome. Adaboost model can be used to design decision support systems or rapid diagnostic tools that utilise the internet or mobile devices as platforms in the diagnosis of malaria. The application of the present work as potentials in reduction of misdiagnosis incidences, reducing the mortality due to malaria and improving the overall public health of people residing in malaria endemic areas.